Cadence analysis for a video signal having an interlaced format

ABSTRACT

An interlaced video signal can include content of different types, such as interlaced content and progressive content. The progressive content may have different cadences according to the ratio between the frame rate of the progressive content and the field rate of the interlaced video signal. Cadence analysis is performed to identify the cadence of the video signal and/or to determine field pairings when progressive content is included. As described herein, motion information (e.g. motion vectors) for blocks of fields of a video signal can be used for the cadence analysis. The use of motion information provides a robust method of performing cadence analysis.

BACKGROUND

Video signals may have an interlaced format comprising a sequence offields, where each field represents a subset of the lines of a frame ofvideo content. The video signal may include video content of differenttypes, e.g. content which has an interlaced content type or contentwhich has a progressive content type. The interlaced video signalincludes two complementary types of fields which together represent allof the lines of a frame of the video content, albeit maybe not for eachframe (i.e. time instance) of the content. For example a first field mayrepresent the odd lines of a first frame, and then the next field in thevideo signal may represent the even lines of a frame which, depending onthe type of content in the video signal, may be the same frame or adifferent frame to the first frame.

A de-interlacer is used to generate any missing lines of the videosignal at each time instance to thereby recover full frames of the videosignal. Compared to including full frames at each time instance in thevideo signal, interlacing allows fewer lines (and therefore less data)to be included in the video signal without a reduction in the frame rateof the video signal (although interlacing may introduce some extracomplications and/or errors). Methods for implementing de-interlacingare known in the art (e.g. using interpolation), but in order for ade-interlacer to operate correctly, it needs to know the native temporalpatterns of the video content in the video signal, i.e. the cadence ofthe video signal, as explained below. Therefore, a video processing unitmay implement cadence detection in order to identify the cadence of avideo signal. Having detected the cadence of the video signal thede-interlacer can correctly apply any necessary de-interlacing to thefields of the video signal.

Most video broadcast systems transmit video signals in an interlacedformat, which includes a sequence of fields at a rate of, for example,50 or 60 fields per second. As described above, the video signal mayinclude video content of different types, e.g. interlaced orprogressive. For example, films are usually shot at 24 or 25 frames persecond, while video contents for TV may be shot at 50 or 60 frames persecond. However, for each of these content types, the video signal isoften broadcast in an interlaced format, such that a sequence of fieldsare included in the video signal at a field rate of e.g. 50 or 60 fieldsper second, with each field representing alternately the even and oddlines of the frames.

FIGS. 1a to 1d show how a sequence of frames (A, B, C, D, . . . ) isrepresented by the fields of the video signal in different exampleswhich have different cadences. FIG. 1a shows a sequence of fields 102which represent interlaced content for frames A to J. Each of the framesA to J represents a different time instance in the video content. Thetop line of fields in the sequence 102 represent the odd lines of theframes A, C, E, G and I, and the bottom line of fields in the sequence102 represent the even lines of the frames B, D, F, H and J. The cadenceof the sequence of fields 102 represents interlaced content (i.e. eachfield represents a different time instance). In order to determine theframes of the video content, a de-interlacer will generate thecomplementary fields at each time instance to thereby generate asequence of frames 104. The de-interlacer generates the complementaryfields A′ to J′ to represent the lines of the fields which are notincluded in the sequence 102, such that the resulting sequence of frames104 includes all of the lines of the frames (both odd and even lines)for each of the frames A to J. It is noted that the complementary fieldsA′ to J′ include approximations of the original lines of the frames. Theframes 104 can then be output (e.g. at 60 frames per second) to therebyoutput the frames A to J of the video signal. Methods for performingde-interlacing are known in the art.

FIG. 1b shows a sequence of fields 106 which represent progressivecontent for frames A to E, with a 2:2 cadence (which may simply bewritten as a 22 cadence). Progressive content often comprises imagesderived from film. For example, the video content may be a film at 25frames per second and the video signal may have 50 fields per secondsuch that each frame of the film can be represented over two fields ofthe interlaced video signal. Therefore, as shown in FIG. 1b , each frameis split into a top field (e.g. including odd lines) and a bottom field(e.g. including even lines). Therefore, the first two fields in thesequence 106 both relate to the same time instance, i.e. to frame A,then the next two fields both relate to the next time instance, i.e. toframe B, and so on. Fields may then be paired together accordingly tothereby generate a sequence of frames 108 which is to be output at 50frames per second. As can be seen in FIG. 1b , each frame of theprogressive video signal is repeated in the sequence of frames 108 suchthat the frames are output at the correct rate.

FIG. 1c shows another example in which a sequence of fields 110represents progressive content for frames A to D, but this time with a32 cadence. For example, the video content may be a film at 24 framesper second and the video signal may have 60 fields per second such thattwo frames of the film can be represented over five fields of theinterlaced video signal. Therefore, as shown in FIG. 1c , a first frame(e.g. frame A) is split into a top field (e.g. including odd lines) anda bottom field (e.g. including even lines) whilst the next frame (e.g.frame B) has three fields relating to it: a top field then a bottomfield and then a repeated top field. The sequence repeats as shown inFIG. 1c such that two fields relate to frame C and three fields relateto frame D. Fields may then be paired together accordingly to therebygenerate a sequence of frames 112 which is to be output at 60 frames persecond. As can be seen in FIG. 1c , frames A and C of the video signalare included twice in the sequence of frames 112 whilst frames B and Dof the video signal are included three times in the sequence of frames112, such that, on average, the frames are output at the correct rate.It is relatively straightforward, when the cadence of the video isknown, to discard the repeated frames to recover the original (e.g. 24frames per second) progressive video signal. In some systems theoriginal progressive video signal may then undergo further processingsuch as interpolation, to increase the frame rate for display at, forexample, 60 frames per second.

Similarly to FIG. 1c , FIG. 1d shows another example in which a sequenceof fields 114 represents progressive content for frames A to D, but thistime with a 2332 cadence. As with FIG. 1c , the video content may be afilm at 24 frames per second and the video signal may have 60 fields persecond such that two frames of the film can be represented over fivefields of the interlaced video signal. Therefore, as shown in FIG. 1d ,a first frame (e.g. frame A) is split into a top field (e.g. includingodd lines) and a bottom field (e.g. including even lines) whilst thenext frame (e.g. frame B) has three fields relating to it: a top fieldthen a bottom field and then a repeated top field. In contrast to theexample shown in FIG. 1c , three fields relate to frame C and two fieldsrelate to frame D. Fields may then be paired together accordingly tothereby generate a sequence of frames 116 which is to be output at 60frames per second. As can be seen in FIG. 1d , frames A and D of thevideo signal are included twice in the sequence of frames 112 whilstframes B and C of the video signal are included three times in thesequence of frames 112, such that, on average, the frames are output atthe correct rate.

Many other cadences are possible in a video signal for use withdifferent relationships between the temporal characteristics of thevideo content and the field rate of the video signal. For example, someother possible cadences with progressive video content are 2224, 32322,55, 64 and 8787, and a person skilled in the art will appreciate thatthere are other possible cadences also.

It can be seen that characteristic temporal patterns arise whenprogressive video sequences are transmitted in an interlaced videoformat. Therefore, when a video signal is received, beforede-interlacing is applied, cadence detection is applied in order toidentify the type of content in the video signal (e.g. interlaced orprogressive) and when progressive content is included to identify thecadence of the video signal. When progressive content is included in thevideo signal the identified cadence can be used to recover the originalprogressive frames by re-combining field pairs, and when interlacedcontent is included in the video signal, the de-interlacer can be usedto interpolate the missing lines.

A typical approach to cadence detection is to detect high spatialfrequency artefacts (known as “mouse teeth” artefacts) which are presentwhen combining non-paired fields of a video signal that contains motion.For example, if the bottom field for frame A was combined with the topfield for frame B then because the fields are from different frames(i.e. from different time instances) then some of the content will notalign properly and there will be artefacts where the content does notalign correctly. When there is motion in the content between the twoframes making up the combined image, the artefacts may occur on everyline of the combined image, such that the artefacts (which are the“mouse teeth” artefacts) have a characteristic spatial frequencycorresponding to the number of lines in the image. The presence ofartefacts with this characteristic spatial frequency can be detected.The presence of “mouse teeth” in a combined image indicates that thefield pairing was incorrect and that a different field pairing should beselected. In this way the characteristic temporal patterns in the videosignal can be identified. The temporal patterns in the video signal canthen be used to identify the cadence of the video signal from a list ofknown possible cadences. However, this approach to cadence detection hasthe following limitations: (i) it has a lack of robustness to unknowncadences because it must have an awareness of possible cadences apriori; (ii) it is unlikely to be robust to bad-edits in the videocontent, where the cadence of the video signal is interrupted by editingof the video signal; and (iii) it is not robust to video content withmultiple cadences, for example when a video text overlay (in aninterlaced form) is added over the top of film (which has a progressiveform).

SUMMARY

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used to limit the scope of the claimed subject matter.

De-interlacers can be classified as either motion adaptive or motioncompensated. If a de-interlacer is motion compensated then a motionestimator will be implemented for the purposes of de-interlacing. Theinventor has realised that motion information (e.g. motion vectors) forblocks of fields of a video signal can be used for cadence analysis ofthe video signal. For example, it is particularly suitable to use themotion information for cadence analysis when the cadence analysis isperformed on a video processing unit which already includes a motionestimation module configured to determine motion information forpurposes other than cadence analysis. For example, if a motioncompensated de-interlacer is implemented on a video processing unitwhich makes use of motion vectors then it can be particularly beneficial(e.g. incurring little extra cost) to use the motion vectors on thevideo processing unit for the further purpose of cadence analysis.Therefore, in these examples the motion vectors are not determined forthe sole purpose of performing the cadence analysis.

There is provided a method of processing a video signal in a videoprocessing unit, the video signal having an interlaced format andcomprising a sequence of fields, the method comprising: obtaining motionindicators for blocks of the fields of the video signal; and using theobtained motion indicators to determine cadence signatures which areindicative of one or more cadences of the video signal, wherein thefields of the video signal are to be processed in accordance with thedetermined cadence signatures to thereby determine frames of the videosignal.

For example, a method of processing a video signal in a video processingunit may be provided, wherein the video signal has an interlaced formatand comprises a sequence of fields, and wherein the method comprises:obtaining motion indicators for blocks of the fields of the videosignal; and using the obtained motion indicators to determine cadencesignatures for blocks of a field, wherein the cadence signatures areindicative of one or more cadences of the video signal; creating ahistogram of determined cadence signatures of blocks of the field; anddetermining the position of one or more peaks in the histogram tothereby determine one or more significant cadence signatures in thefield; wherein the fields of the video signal are to be processed inaccordance with the determined one or more significant cadencesignatures to thereby determine frames of the video signal.

As another example, a method of processing a video signal in a videoprocessing unit may be provided, wherein the video signal has aninterlaced format and comprises a sequence of fields, and wherein themethod comprises: obtaining motion indicators for blocks of the fieldsof the video signal; using the obtained motion indicators to determinecadence signatures which are indicative of one or more cadences of thevideo signal; and using the determined cadence signatures to identifyone or more cadences of the video signal by: (i) identifying one or moreprevalent cadence signatures from the determined cadence signatures, and(ii) mapping each of the one or more prevalent cadence signatures to aknown cadence from a set of known cadences which have known cadencesignatures by determining which of the known cadence signatures of theknown cadences has a smallest distance to the prevalent cadencesignature; wherein the fields of the video signal are to be processed inaccordance with the identified one or more cadences to thereby determineframes of the video signal.

This is also provided a video processing unit configured to process avideo signal, the video signal having an interlaced format andcomprising a sequence of fields, the video processing unit beingconfigured to obtain motion indicators for blocks of the fields of thevideo signal, wherein the video processing unit comprises: a cadenceanalysis module configured to use the obtained motion indicators todetermine cadence signatures which are indicative of one or morecadences of the video signal, wherein the video processing unit isconfigured to process the fields of the video signal in accordance withthe determined cadence signatures to thereby determine frames of thevideo signal.

For example, a video processing unit configured to process a videosignal may be provided, wherein the video signal has an interlacedformat and comprises a sequence of fields, the video processing unitbeing configured to obtain motion indicators for blocks of the fields ofthe video signal, wherein the video processing unit comprises: a cadenceanalysis module configured to: use the obtained motion indicators todetermine cadence signatures for blocks of a field, wherein the cadencesignatures are indicative of one or more cadences of the video signal;create a histogram of determined cadence signatures of blocks of thefield; and determine the position of one or more peaks in the histogramto thereby determine one or more significant cadence signatures in thefield; wherein the video processing unit is configured to process thefields of the video signal in accordance with the determined one or moresignificant cadence signatures to thereby determine frames of the videosignal.

As another example, a video processing unit configured to process avideo signal may be provided, wherein the video signal has an interlacedformat and comprises a sequence of fields, the video processing unitbeing configured to obtain motion indicators for blocks of the fields ofthe video signal, wherein the video processing unit comprises: a cadenceanalysis module configured to use the obtained motion indicators todetermine cadence signatures which are indicative of one or morecadences of the video signal, and to use the determined cadencesignatures to identify one or more cadences of the video signal by: (i)identifying one or more prevalent cadence signatures from the determinedcadence signatures, and (ii) mapping each of the one or more prevalentcadence signatures to a known cadence from a set of known cadences whichhave known cadence signatures by determining which of the known cadencesignatures of the known cadences has a smallest distance to theprevalent cadence signature, wherein the video processing unit isconfigured to process the fields of the video signal in accordance withthe identified one or more cadences to thereby determine frames of thevideo signal.

There may be provided computer readable code adapted to perform thesteps of any of the methods described herein when the code is run on acomputer. Furthermore, there may be provided computer readable code forgenerating a video processing unit according to any of examplesdescribed herein. The computer readable code may be encoded on acomputer readable storage medium.

The above features may be combined as appropriate, as would be apparentto a skilled person, and may be combined with any of the aspects of theexamples described herein.

BRIEF DESCRIPTION OF THE DRAWINGS

Examples will now be described in detail with reference to theaccompanying drawings in which:

FIGS. 1a to 1d show how a sequence of frames is represented by thefields of a video signal in different examples which have differentcadences;

FIG. 2 shows a functional block diagram of a video processing unit;

FIG. 3 shows a flow chart for a method of processing a video signal inthe video processing unit;

FIG. 4a shows a representation of the motion of the blocks of fields ofa video signal over a sequence of nine fields in a first example;

FIG. 4b shows the cadence signatures for blocks of a field determinedfrom the motion analysis shown in FIG. 4 a;

FIG. 5a shows a representation of the motion of the blocks of fields ofa video signal over a sequence of nine fields in a second example;

FIG. 5b shows the cadence signatures for blocks of a field determinedfrom the motion analysis shown in FIG. 5 a;

FIG. 6 shows a histogram of the cadence signatures of the progressiveblocks shown in FIG. 5 b;

FIG. 7 is a schematic diagram of a motion estimation module in a firstexample; and

FIG. 8 is a schematic diagram of a motion estimation module in a secondexample.

The accompanying drawings illustrate various examples. The skilledperson will appreciate that the illustrated element boundaries (e.g.,boxes, groups of boxes, or other shapes) in the drawings represent oneexample of the boundaries. It may be that in some examples, one elementmay be designed as multiple elements or that multiple elements may bedesigned as one element. Common reference numerals are used throughoutthe figures, where appropriate, to indicate similar features.

DETAILED DESCRIPTION

Embodiments will now be described by way of example only.

As described above, a video signal having an interlaced format comprisesa sequence of fields, but the video content represented by the fields ofthe video signal may be of different content types and/or differentcadences. When interlaced content is included in the video signal theneach field corresponds to a different time instance (i.e. a differentframe), and a de-interlacer is used to reconstruct full frames at eachtime instance from the sequence of fields. However, when progressivevideo content is represented in an interlaced format, the progressiveframes need to be stored in interlaced fields, and the relationshipbetween the frame rate of the video content and the field rate of thevideo signal determines the cadence of the video signal.

In examples described herein, motion vector information for blocks ofthe fields of the video signal is used to derive a cadence signaturewhich is indicative of one or more cadences of the video signal. In someexamples, the cadence signatures are used to identify the cadence of thevideo signal from a set of known cadences. In other examples, thecadence signatures may be used to derive field pairings withoutnecessarily explicitly detecting the cadence of the video signal. Bydetecting the cadence of the video signal, the content type of thecontent in the video signal will be detected (e.g. as interlaced contentor progressive content) because interlaced content has a differentcadence to progressive content.

Motion estimation is a known process which can be used to determine amotion vector for each block of a field which describes the motion ofthat block relative to the content of another field from the videosignal. For example, where motion vectors are to be determined forblocks of a current field, a backward motion estimator may compareblocks of the current field with content from a previous (e.g. theimmediately preceding) field from the video signal in order to determinea motion vector (referred to as a backward motion vector) describing themotion of the content between the previous field and the current field.As another example, a forward motion estimator may compare blocks of thecurrent field with content from a subsequent (e.g. the immediatelyfollowing) field from the video signal in order to determine a motionvector (referred to as a forward motion vector) describing the motion ofthe content between the current field and the subsequent field. Asanother example, a bidirectional motion estimator may compare content ofthe previous field with content from the subsequent field from the videosignal in order to determine a motion vector for the block of thecurrent field (referred to as a bidirectional motion vector) describingthe motion of the content between the previous field and the subsequentfield. Further details of motion estimation are known to those skilledin the art and for conciseness are not included in this description.

Motion estimation is used for some processing steps which may beperformed on a video signal, e.g. for de-interlacing, frame rateconversion or scaling, etc. However, typically in the prior art, thecadence of the video signal would be detected before any motionestimation is performed because until cadence detection has beenperformed, a video processing unit does not know how to interpret avideo signal. For example, motion estimation should not attempt toestimate motion between repeated frames of progressive content, e.g. forframe rate conversion, because the zero motion between repeatedinstances of the same frame does not represent the true motion ofobjects in the scene. However, in examples described herein, motionestimation is performed and used to derive a cadence signature, whichcan then be used to detect the cadence of the video signal and/or toappropriately match progressive field pairs.

FIG. 2 shows a video processing unit 202 which is configured to processa video signal. The video signal is received at the video processingunit 202 having an interlaced format and comprising a sequence offields, and the video processing unit 202 performs video processingoperations on the video signal to thereby determine and output aprocessed video signal. The video processing unit 202 comprises acadence analysis module 204, a motion estimation module 206 and a frameprocessing module 209. The video processing unit 202 may also optionallycomprise one or more further processing modules 212. The motionestimation module 206 comprises one or more motion estimators 208. Themotion estimation module 206 is an example of a motion analysis modulewhich could be used. As described below, in other more general examples,a motion analysis module may analyse motion in the video signal withoutactually estimating the motion (e.g. without actually determining motionvectors), and the results of such motion analysis could be used forcadence analysis in a similar manner to that in which the results of themotion estimation are used in the examples described in detail herein.The frame processing module 209 may include one or both of ade-interlacer and a decimator. In the example shown in FIG. 2 the frameprocessing module 209 includes both the de-interlacer 210 and thedecimator 211. The de-interlacer 210 is configured to implementinterpolation to approximate missing lines of an interlaced videosignal. The decimator 211 is configured to remove repeated frames from avideo signal which includes progressive content. Therefore the frameprocessing module 209 may be capable of performing one or both of thede-interlacing and decimating functions.

The modules of the video processing unit 202 shown in FIG. 2 may beimplemented in hardware, software or a combination thereof. The cadenceanalysis module 204, the motion estimation module 206 and the frameprocessing module 209 are arranged to receive the interlaced videosignal. An output of the cadence analysis module 204 is coupled to aninput of the frame processing module 209. An output of the motionestimation module 206 is coupled to an input of the cadence analysismodule 204 and to an input of the frame processing module 209. An outputof the frame processing module 209 is arranged to provide a processedvideo signal to which further processing may be applied by the furtherprocessing module(s) 212 before the video signal is output from thevideo processing unit 202. The processed video signal output from thevideo processing unit 202 may be used in any suitable manner, e.g. itmay be displayed on a display or stored in a memory.

The operation of the video processing unit 202 is described withreference to the flow chart shown in FIG. 3.

An interlaced video signal is received at the video processing unit 202.For example, the video signal may be a broadcast video signal (e.g. atelevision signal) having an interlaced format wherein the videoprocessing unit 202 may for example be implemented in a set top boxwhich receives the video signal and passes the video signal to the videoprocessing unit 202 for processing. The interlaced video signal ispassed to the cadence analysis module 204 and to the motion estimationmodule 206.

In step S302 the motion estimation module 206 determines motion vectorsfor blocks of the fields of the video signal. A block for which a motionvector is determined is a group of pixels of a field, which in generalmay have any suitable size and shape, such as a 4×4, 8×8, 16×8, or 16×16block of pixels. Methods of determining motion vectors for blocks offields of the video signal are known in the art, and therefore thoseskilled in the art will readily understand how to implement motiondetection without further explanation. For example, the motion estimator208 may be a backwards motion estimator, which as described above,determines motion vectors for blocks of a current field by comparing thecontents of the blocks with the contents of a previous field of thevideo signal. Additionally or alternatively, other types of motionestimator may be used such as a forwards motion estimator and/or abidirectional motion estimator as described in more detail below withreference to FIG. 8. The motion vectors are output from the motionestimation module 206 and received by the cadence analysis module 204.

In step S304 the cadence analysis module 204 assigns a binary motionflag to each block of the fields of the video signal based on the motionvectors received from the motion estimation module 206. For example, themagnitude of the motion vector for a block may be used to determine thebinary flag to be assigned to that block, such that if the motion vectorfor the block in a particular field is indicative of there being nomotion then the binary flag assigned to the block in the particularfield is a first binary value (in the examples described herein thefirst binary value is zero, but in other examples the first binary valuecould be one), and if the motion vector for the block in the particularfield is indicative of there being some motion then the binary flagassigned to the block in the particular field is a second binary value(in the examples described herein the second binary value is one, but inother examples the second binary value could be zero). A motion vectormay be considered to be indicative of there being no motion if itsmagnitude is not greater than a threshold, which may be close to, orequal to, zero; whilst a motion vector may be considered to beindicative of there being some motion if its magnitude is greater thanthe threshold.

FIG. 4a shows an example of the binary flags which are assigned to theblocks in a sequence of nine consecutive fields (402 ₁ to 402 ₉) of avideo signal which comprises interlaced video content. In this example,the field 402 ₉ is a current field and the fields 402 ₁ to 402 ₈ are theeight fields immediately preceding the current field 402 ₉ in the videosignal. In the example shown in FIG. 4a each field includes a 6×15 arrayof blocks for which a binary flag (either a one to indicate some motionor a zero to indicate no motion) has been assigned based on the motionvectors for the blocks. For the purposes of explanation, a particularblock 404 in the sequence of fields 402 ₁ to 402 ₉ is highlighted byhatching.

In step S306, for each block, the binary flags over the sequence ofconsecutive fields (e.g. over nine consecutive fields) are concatenatedto thereby determine a cadence signature for the block in the currentfield 402 ₉. In other examples, a different number of fields (e.g. otherthan nine) may be included in the sequence of fields from which thebinary flags are concatenated to determine the cadence signatures. Thegreater the number of fields included in the sequence, the more data isneeded to be stored in order to generate the cadence signatures, but thegreater the likelihood that the cadence signatures can be used tocorrectly identify the cadence of the video signal, as described in moredetail below.

FIG. 4b shows the results (represented in decimal form) of theconcatenations of the binary flags in step S306 for the current field402 ₉ as an array of cadence signatures 406. That is, the numbers shownin the array 406 are the cadence signatures for the respective blocks ofthe current field 402 ₉. As an example, the concatenation of the binaryflags for block 404 over the nine fields yields a cadence signature of111111110 as a binary string which is 510 in decimal notation. The sameconcatenation is performed for each of the blocks in the current field402 ₉ to give the cadence signatures shown in FIG. 4b for the respectiveblocks.

FIGS. 5a and 5b show an example of the binary flags which are assignedto the blocks in a sequence of nine consecutive fields (502 ₁ to 502 ₉)of a video signal which comprises progressive video content having a 32cadence. In this example, the field 502 ₉ is a current field and thefields 502 ₁ to 502 ₈ are the eight fields immediately preceding thecurrent field 502 ₉ in the video signal. As with FIG. 4a , in theexample shown in FIG. 5a , each field includes a 6×15 array of blocksfor which a binary flag has been assigned based on the motion vectorsfor the blocks. For the purposes of explanation, a particular block 504in the sequence of fields 502 ₁ to 502 ₉ is highlighted by hatching.

FIG. 5b shows the results (represented in decimal form) of theconcatenations of the binary flags in step S306 for the current field502 ₉ as an array of cadence signatures 506. That is, the numbers shownin the array 506 are the cadence signatures for the respective blocks ofthe current field 502 ₉. As an example, the concatenation of the binaryflags for block 504 over the nine fields yields a cadence signature of001010010 as a binary string which is 82 in decimal notation. The sameconcatenation is performed for each of the blocks in the current field502 ₉ to give the cadence signatures shown in FIG. 5b for the respectiveblocks.

Since in the examples shown in FIGS. 4a, 4b, 5a and 5b nine flags areconcatenated to form the cadence signatures, the cadence signatures arein the range from 0 to 511. In general, if N binary flags areconcatenated to form the cadence signatures, the cadence signatures arein the range from 0 to 2^(N)−1. The cadence signatures shown in FIGS. 4band 5b are indicative of the cadence of the respective video signals.

The video signal includes video content of one or more types from a setof available video content types which have different cadences. Forexample, the set of available video content types includes an interlacedcontent type and a progressive content type. The determined cadencesignatures are used to determine the one or more types of the videocontent in the video signal. In particular, as an example, an analysisof the cadence signatures may be performed in order to assign each ofthe blocks in the current field (e.g. field 502 ₉) to one of four types:(i) static content type; (ii) interlaced content type; (iii) progressivecontent type; or (iv) low confidence. In step S308 a global analysis ofthe blocks in the current field is performed to analyse the cadencesignatures to thereby determine which type(s) of content are present inthe current field (e.g. field 502 ₉).

That is, each of the blocks in the current field is assigned to one ofthe types based on the determined cadence signature for that block. Itis noted that if the content in a block of the video signal is notchanging over a number of frames then the motion vectors for that blockwill be zero regardless of the video content type and the methoddescribed herein does not determine whether the content of that block isinterlaced or progressive and as such the block is assigned the staticcontent type. If the content in a block is truly static then, for thepurposes of this description, it does not matter whether the block isinterpreted as interlaced content or progressive content since in bothcases the content of the block can be reconstructed correctly forinclusion in the final processed video signal. Furthermore, in somesituations the cadence signature for a block might not provide a goodindication of a particular content type, in which case the block can beassigned to a low confidence type to indicate that the content type ofthe block has not been determined with confidence. The use of the lowconfidence type reduces the likelihood of processing a block inaccordance with the wrong content type.

When the motion estimator 208 determines the motion vectors for theblocks of the fields of the video signal according to known motionestimation techniques, it may determine a confidence indicationindicating a level of confidence that the motion vector is correct. Forexample, the motion estimator 208 may set a low confidence indicationdepending on content characteristics and disparity with neighbouringmotion vectors. The confidence indications may be passed from the motionestimation module 206 to the cadence analysis module 204. The cadenceanalysis module 204 may assign a block to the low confidence type basedon the confidence indications received from the motion estimation module206. For example, the cadence analysis module 204 may assign a block tothe low confidence type based on the current motion estimationconfidence for the block. In some examples (described in more detailbelow with reference to FIG. 8) the motion estimation module 206comprises: (i) a backward motion estimator configured to generatebackward motion vectors (“bwd”) for blocks of a current field; (ii) abidirectional motion estimator configured to generate bidirectionalmotion vectors (“bidir”) for blocks of the current field; and (iii) aforward motion estimator configured to generate forward motion vectors(“fwd”) for blocks of the current field; and in these examples thecadence analysis module 204 may assign a block to the low confidencetype if |(fwd|bwd)|>|bidir|.

If a block is not assigned to the low confidence type then the block maybe assigned to a content type based on the number of bits (N_(motion))of the cadence signature which are a 1 (i.e. indicating that motion ispresent). It should be apparent that in some other examples the numberof zeroes could instead be used. However, in the examples describedherein, if the number of ones in the cadence signature (N_(motion)) fora block is below a lower threshold then the block is assigned to thestatic content type and if N_(motion) for a block is above an upperthreshold then the block is assigned to the interlaced content type. Forexample, where N=9, the lower threshold may be 1 and the upper thresholdmay be 5. In a more general example, the lower threshold may be given by

$\left\lfloor \frac{N}{8} \right\rfloor$and the upper threshold may be given by

$\left\lceil \frac{N}{2} \right\rceil.$In this way, if more than

$\left\lceil \frac{N}{2} \right\rceil$of the N bits of the cadence signature for a block are a 1 then theblock is classified as an interlaced block. This makes sense becauseeach frame of progressive content will relate to at least twoconsecutive fields of the interlaced video signal, such that thegreatest number of ones in a cadence signature of length N, forprogressive content, is

$\left\lceil \frac{N}{2} \right\rceil.$Furthermore, since each field of an interlaced sequence is from adifferent time instance, if motion is present in the scene it will bedetected between each pair of consecutive fields. Therefore, the numberof ones in a cadence signature of length N, for interlaced content, mayapproach N. So if the number of ones in a cadence signature exceeds theupper threshold then it is determined that the block does not relate toprogressive content, and so is assigned the interlaced content type. Ifthe number of ones in the cadence signature (N_(motion)) is less thanthe lower threshold this indicates that there is no motion (or verylittle motion) in the content of the block over the previous N fieldsand as such the block is assigned the static content type.

The remaining blocks, i.e. the blocks for which the low confidence typehas not been assigned and for which N_(motion) is not less than thelower threshold nor greater than the upper threshold, are likely to beblocks of progressive content. However, a further check may be performedwhich includes determining the number of times that consecutive ones arefound in the cadence signature (N_(consecutive)) and to check that thisis below a further threshold (Th_(consecutive)). It can be appreciatedthat, if no errors are present, progressive content should not includeany consecutive ones in the cadence signature because the content shouldnot change twice in two consecutive fields. However, in some cases (e.g.if errors are present or if a bad edit such as a scene cut in the videocontent results in progressive content which does change twice in twoconsecutive fields of the video signal) progressive content mayoccasionally result in content which changes twice in two consecutivefields. Therefore, in the example in which N=9, the threshold,Th_(consecutive), is set to be 2. As a general example, Th_(consecutive)may be given by

$\left\lceil \frac{N}{8} \right\rceil.$If N_(consecutive)<Th_(consecutive) then the block is assigned to theprogressive content type. However, if N_(consecutive)≤Th_(consecutive)then this is indicative of there being lots of errors present and assuch the block is assigned to the low confidence type, rather thanrisking assigning the block to the progressive type incorrectly. Thevalues of the thresholds described herein are implementation dependentand may be configurable.

As an example, in relation to FIGS. 4a and 4b the block 404 ₉ which hasa cadence signature of 510 will be assigned to the interlaced contenttype (assuming that the motion estimation module 206 did not indicatethat the motion vectors for more than 2 of the blocks 404 ₁ to 404 ₉ hada low confidence). This is because the number of ones in the cadencesignature for block 404 over the nine fields (which has a decimal valueof 510 and a binary value of 111111110) is eight, i.e. it is greaterthan five.

As another example, in relation to FIGS. 5a and 5b the block 504 ₉ whichhas a cadence signature of 82 will be assigned to the progressivecontent type (assuming that the motion estimation module 206 did notindicate that the motion vectors for more than 2 of the blocks 504 ₁ to504 ₉ had a low confidence). This is because the number of ones in thecadence signature for block 504 over the nine fields (which has adecimal value of 82 and a binary value of 001010010) is three, i.e. itis not less than two nor greater than five, and the number ofconsecutive ones in the cadence signature is zero, i.e. it is less than2. Furthermore, all of the blocks with cadence signatures of 82 will beassigned to the progressive content type. It can be appreciated that byapplying the rules described above, the blocks shown in FIG. 5b withcadence signatures of 0 will be assigned to the static content type;whilst the blocks shown in FIG. 5b with cadence signatures of 511 willbe assigned to the interlaced content type.

Once each of the blocks of the current field have been assigned to oneof the four types, then in step S310 it is determined whether the numberof progressive blocks in the current field (e.g. field 502 ₉) exceeds athreshold. That is, the number of blocks in the field which are assignedto the progressive content type is determined and compared to athreshold. The threshold may, for example, be 5 in the case of theexamples shown in FIGS. 4a to 5b in which there are 90 blocks in afield. In a general example, the threshold may be approximately atwentieth of the number of blocks in each field. Another test may beperformed to determine whether the result of

$\frac{P}{{I} + {P}}$is greater than a second threshold (e.g. where the second threshold maybe 0.1, as an example), where |P| is the number of progressive blocks inthe current field and |I| is the number of interlaced blocks in thecurrent field. If the number of progressive blocks in the current fieldexceeds the threshold(s) then there is sufficient progressive content inthe current field for it to be useful to determine the cadence and/orfield pairings of the progressive blocks of the current field.Therefore, in this example the method passes to step S312 and to stepS314 when the test(s) of step S310 is(are) passed. As examples, thefields shown in FIGS. 5a and 5b will include more than five progressiveblocks whereas the fields shown in FIGS. 4a and 4b will not include morethan 5 progressive blocks. It is noted that there is a bias towardscaution when detecting progressive content because it is less visuallyobjectionable to treat progressive content as interlaced thanvice-versa.

In step S312 the determined cadence signatures are used to derive fieldpairings of consecutive fields which relate to the same time instance(i.e. the same frame) of the video content. That is, for blocks whichare determined to include progressive content, the field pairings aredetermined based on the cadence signatures for the blocks. For example,this can be done by considering the position of zeroes in the cadencesignatures for blocks assigned to the progressive content type because azero in the cadence signature indicates that there has been no motionbetween two consecutive fields, which may therefore be an indicationthat the fields relate to the same frame of the progressive content.Therefore, the field pairings can be derived by finding the zeroes (orin alternative examples the ones, where ones are used to indicate thatno motion has occurred) in the most recent elements of the cadencesignatures for the blocks of a field.

For example, if the motion vectors are backwards motion vectors whichcompare motion between the current field (e.g. field 502 ₉) and theprevious field (e.g. field 502 ₈) then a zero in the binary motion flagof the current field (e.g. a zero for flag 504 ₉) indicates that therehas been no motion in the block 504 between fields 502 ₈ and 502 ₉.Therefore, in this case, if all of the blocks in the field 502 ₉ have abinary flag of zero then the fields 502 ₈ and 502 ₉ are deemed to bematching fields (i.e. relate to the same frame of the progressivecontent). Therefore, in this case fields 502 ₈ and 502 ₉ are pairedtogether in step S312. As another example, which is different to theexample shown in FIG. 5a , if the motion vectors are forwards motionvectors which compare motion between a current field and the next fieldthen a zero in the binary motion flag for a block of the current fieldindicates that there is no motion in the block between current field andthe next field. Therefore, in this case the current and next fields aredeemed to be matching fields (i.e. relate to the same frame of theprogressive content). Therefore, in this case, if all of the blocks inthe current field have a binary flag of zero then the current and nextfields are paired together in step S312. It is noted that the term“field pairing” is used to indicate that two or more (not necessarilyjust two) fields of the video signal relate to the same frame of theprogressive content. For example, three of the fields in the videosignal 110 shown in FIG. 1c relate to frame B and all three of thesefields will be deemed to be matching and this will be indicated in thefield pairings derived in step S312.

It can be appreciated that the field pairings are derived for theprogressive content of the video signal in step S312 without necessarilydetermining the cadence of the progressive content. In some examplesthis could be the end of the analysis performed by the cadence analysismodule 204, if the field pairings are all that are needed to bedetermined by the cadence analysis. In these examples, the fieldpairings are derived based on the motion vectors for the blocks of thefields of the video signal. It is noted that the field pairingtechnique, which is based on the motion vectors that were determined forthe blocks, is robust to bad edits of video content, e.g. where a sceneis cut thereby separating a matching pair of fields, because the fieldpairing may be determined for each field based on the binary flags ofthe blocks representing the most recent motion vectors. This is incontrast to a method which may determine a field pairing pattern andthen assume that this pattern will continue which could encounterproblems at scene cuts which separate a matching pair of fields.

In other examples, in addition to or as an alternative to step S312,steps S314 to S320 described below are implemented to identify thecadence of the blocks of the current field based on the cadencesignatures.

In step S314 a histogram is created of the cadence signatures of theblocks of the current field which are assigned to the progressivecontent type. The histogram is referred to as a progressive cadencesignature histogram. FIG. 6 shows an example of the progressive cadencesignature histogram that is determined using the cadence signatures ofthe progressive blocks shown in the example shown in FIGS. 5a and 5b .As described above, in the example shown in FIG. 5b , in the currentfield 502 ₉ it is only the blocks with cadence signatures of 82 thatwill have been assigned to the progressive content type and thereforeall of the blocks assigned to the progressive content type in thecurrent field have a cadence signature of 82 in this example. As such,the histogram shown in FIG. 6 shows a single peak at a cadence signatureof 82 and is zero at every other position. This is a simplified examplefor the purposes of illustration, and in other examples it is likelythat some other cadence signatures will be included such that there maybe other smaller peaks in the histogram and/or some noise in thehistogram. For example, if there happens to be no motion in a particularblock between a pair of frames of the video content then the cadencesignature for the particular block would not match the cadence of theprogressive content so may show up as a peak at a different position inthe progressive cadence signature histogram.

However, there is likely to be a significant peak in the progressivecadence signature histogram at a position which does match the cadenceof the progressive content, and the other peaks are likely to be smallerthan the significant peak. Furthermore, if different regions of theframes of the video content have different cadences (e.g. when there isa mix of different video content being displayed, for example if sometext is overlaid on top of a film) then there may be a significant peak(i.e. a large peak) in the progressive cadence signature histogram ateach position which matches one of the different cadences which arepresent in the video content. A peak is determined to be significant andtherefore indicative of a cadence in the video signal if the peak ishigher than a threshold in the progressive cadence signature histogram.This threshold is set to avoid identifying erroneous small peaks in thehistogram as being indicative of a cadence, and for example may be setat 5% of the number of blocks which are determined to have theprogressive content type. In more complex examples, the threshold may beset based on a number of factors, including one or more of: (i) theproportion of blocks which are progressive, ii) the proportion of blocksof the dominant cadences, and iii) some hysteresis so that the methodtakes a number of frames to start detecting a cadence being present, atwhich point the required thresholds to continue detecting the cadenceare lowered.

In step S316 the position of one or more peaks in the progressivecadence signature histogram is determined to thereby determine one ormore significant (or “prevalent”) cadence signatures in the field.

In step S318, the cadence signatures identified in step S316 are mappedto known cadences, such that one or more cadences of the video signalare identified. That is, the significant peaks in the progressivecadence signature histogram correspond to cadences which are present inthe video signal. In order to implement step S318 the cadence analysismodule 204 may use a set of known cadences which have known cadencesignatures. In this case, the cadence analysis module 204 determineswhich of the known cadence signatures of the known cadences has thegreatest similarity with a significant cadence signature from thehistogram to thereby map the significant cadence signature to a knowncadence. This is done for each of the cadence signatures that wereidentified from the histogram in step S316.

For example, the table below shows the known nine-bit cadence signaturesfor known cadences of 22, 2224, 32 and 2332:

Binary Cadence Decimal Cadence Cadence Signature Signature 22 010101010170 22 101010101 341 2224 010101000 168 2224 101010001 337 2224010100010 162 2224 101000101 325 2224 0110001010 138 2224 100010101 2772224 000101010 42 2224 001010101 85 2224 010101010 170 2224 101010100340 32 010010100 148 32 100101001 297 32 001010010 82 32 010100101 16532 101001010 330 2332 010010010 146 2332 100100101 293 2332 001001010 742332 010010101 149 2332 100101010 298 2332 001010100 84 2332 010101001169 2332 101010010 338 2332 010100100 164 2332 101001001 329

So if the cadence analysis module 204 is limited to identifying the 22,2224, 32 and 2332 cadences then the cadence analysis module 204 maystore a look-up table for the values in the table shown above which canbe used to link cadence signatures to specific cadences. For example,the current field 502 ₉ in the example shown in FIG. 5a has asignificant cadence signature of 82 and so the cadence analysis module204 can use the table shown above to map this cadence signature to a 32cadence. It is noted that in the table shown above, both the 22 and the2224 cadences may result in a nine-bit cadence signature of 170. One wayto differentiate between the two cadences would be to increase thenumber of bits used in the cadence signatures. For example, if ten bitswere used for the cadence signatures then the cadences 22 and 2224 wouldnot result in the same cadence signatures. Another way to address thisproblem without increasing the number of bits used for the cadencesignatures is to store a history of the significant cadence signaturesover a sequence of fields. For example, if the significant cadencesignatures in a sequence of fields are 170 then 341, it is likely thatthe cadence of the video signal is 22, whereas if the significantcadence signatures in a sequence of fields are 170 then 340, it islikely that the cadence of the video signal is 2224. Therefore, ahistory of significant cadence signatures over a sequence of fields canbe used to distinguish between cadences which happen to result in thesame cadence signatures for one particular field. This means that inthese examples, at least some of the significant cadence signatures arestored from a previous field for use with a current field in order tocorrectly identify the cadences which are present in the current field.

Other cadences, such as 32322, 55, 64 and 8787 may be included in thetable of known cadences and their cadence signatures. This allows thecadence analysis module 204 to identify the other cadences in the videosignal also. However, the inclusion of more cadences in the table willincrease the number of cadence signatures which are not unique to acadence, and will therefore increase the need to distinguish betweencadences having the same cadence signature, e.g. as described above bystoring a history of the significant cadence signatures for a sequenceof fields or by increasing the number of bits in the cadence signatures.Therefore, the table of known cadences might be configured to store theknown cadence signatures only for cadences which are likely to occur inthe video signal. The table of known cadences used by the cadenceanalysis module 204 may be configurable.

The likelihood of different cadences occurring may vary depending uponthe system in which the video processing unit 202 is implemented. Forexample, if the video processing unit 202 is implemented in a set topbox which is arranged to receive broadcast video signals which are to beoutput at 60 Hz then likely cadences for progressive content included inthe video signal are 32 and 2332 for films having a frame rate of 24frames per second and 32322 for films having a frame rate of 25 framesper second. So, in these examples, the cadence analysis module 204 maybe configured to have a table which includes the cadence signatures forthe 32, 2332 and 32322 cadences but might not include cadencessignatures for other, less likely cadences. In another example, if thevideo processing unit 202 is implemented in a set top box which isarranged to receive broadcast video signals which are to be output at 50Hz then a likely cadence for progressive content included in the videosignal is 22 for films having a frame rate of 25 frames per second. So,in these examples, the cadence analysis module 204 may be configured tohave a table which includes the cadence signatures for the 22 cadencebut might not include cadences signatures for other, less likelycadences. Some devices may be configured to receive all content types,but if a device is dealing with 50 Hz then it may use one table whichincludes the cadence signatures for the cadences that are relevant tooutputting at 50 Hz (e.g. the 32, 2332 and 32322 cadences); and if thedevice is dealing with 60 Hz then it may use another table whichincludes the cadence signature(s) for the cadence(s) that is(are)relevant to outputting at 60 Hz (e.g. the 22 cadence).

It is possible that a significant cadence signature determined from theprogressive cadence signature histogram does not exactly match one ofthe known cadences from the table. In which case the cadence analysismodule 204 will map the significant cadence signature to a known cadencefrom the table by determining which of the known cadence signatures fromthe table has the greatest similarity with the significant cadencesignature. This may comprise determining which of the known cadencesignatures of the known cadences has a smallest distance to thesignificant cadence signature. The smallest distance may for example bea smallest Hamming distance, but it is noted that in other examples, adifferent type of distance, other than a Hamming distance, may be usedto indicate measures of similarity between a significant cadencesignature in the field and the known cadence signatures.

A person skilled in the art would know how to determine a Hammingdistance. A Hamming distance between a significant cadence signature anda known cadence signature indicates how many bits are different betweenthe two cadence signatures. Therefore, the smallest Hamming distancewill correspond to the minimum number of bits that need to be changed tomap the significant cadence signature to a known cadence signature. Ifmore than one of the known cadence signatures have the smallest Hammingdistance to a significant cadence signature then a history of a sequenceof fields may be considered in order to resolve the ambiguity asdescribed above. In other examples, an asymmetric weighting could beused to determine a distance measure whereby a particular bit in acadence signature which is different to the corresponding bit in a knowncadence signature is assigned a different weighting if the particularbit is a zero compared to if the particular bit is a one. Anasymmetrically weighted distance measure such as this may be usefulbecause there is a difference between a ‘1’ that should be a ‘0’ and a‘0’ that should be a ‘1’ in a cadence signature. For example, a bit canchange to zero just by there being no motion in the content. Incontrast, a one where a zero is expected requires something moresignificant, e.g. an error by the motion estimator.

In the examples described above, the cadence analysis module 204 uses aset of known cadences which have known cadence signatures in order toimplement step S318. In other examples, there might not be a set ofknown cadences and the cadence analysis module 204 may select thesignificant peak(s) in the progressive cadence signature histogram andmap these onto cadences with no knowledge of which cadences are likely.This method is likely to be less reliable than using a set of knowncadences as described above but it is more flexible to detecting unusualcadences, and does not require the set of known cadences to be stored.

Therefore, following step S318 the cadence analysis module 204 hasidentified a set of one or more cadences of progressive content that isincluded in the current field, but it is not known which of the blocksof the current field have which cadence. The method may end at thispoint if for example, all of the blocks in the current field haveprogressive content and if there is only one significant cadenceidentified in the progressive cadence signature histogram. In that caseall of the blocks of the current field can be assigned to theprogressive content type having the cadence identified in step S318.

However, in other examples, it is not the case that all of the blocks ofa field map to a single progressive cadence. Furthermore, it is knownfrom step S308 whether there are any blocks of an interlaced contenttype or a static content type. A cadence signature of all ones (e.g. 511for a nine-bit cadence signature) corresponds to a cadence which isindicative of an interlaced content type, whilst a cadence signature ofall zeroes corresponds to a cadence which is indicative of a staticcontent type. In step S320 a local analysis of the cadence signatures ofeach block in the current field is performed to assign each block to aninterlaced content type, a static content type or to a progressivecontent type having one of the cadences that was identified as beingpresent within the current field in step S318. That is, for each blockof the current field, step S320 includes determining a respectivemeasure of the similarity between the cadence signature of the block andeach of: (i) the known cadence signatures of the known cadences of theprogressive blocks identified in step S318, (ii) a cadence signatureindicative of interlaced content (i.e. all ones), and (iii) a cadencesignature indicative of static content (i.e. all zeroes). Then eachblock is assigned the cadence and/or content type which has the greatestsimilarity according to the determined measures of similarity. Themeasures of similarity may for example be Hamming distances. It is notedthat if, in step S310, it was determined that the number of progressiveblocks in the current field does not exceed the threshold then themethod passes from step S310 to step S320 whereby the blocks are thenassigned to an interlaced content type or a static content type.

The example shown in FIG. 5b shows that most of the blocks of thecurrent field 502 ₉ have a cadence signature of 82 which corresponds toa progressive content type with a cadence of 32. However, some of theblocks have a cadence signature of 0 which corresponds to a cadence ofthe static content type and some of the blocks have a cadence signatureof 511 which corresponds to a cadence of the interlaced content type.For example, the content could be a film which has a progressive contenttype, but the bottom left corner of the picture may include staticcontent and there may be some text overlaid on top of the film in thebottom right hand corner which has an interlaced type. The methodsdescribed herein allow the content types and cadences of the differentregions of the current field 502 ₉ to be correctly identified such thatall of the blocks of the field can be correctly processed. This is animprovement over the prior art “mouse teeth” method mentioned in thebackground section above which encounters problems with the situation ofthere being multiple cadences (e.g. multiple content types) within afield. Motion estimation techniques provide motion vectors for blocks ofthe fields and therefore provide an elegant solution to determining thecadence and/or content type for individual blocks of a field.

Following the method of cadence analysis shown in FIG. 3, the results ofthe cadence analysis are used in the video processing unit 202 toprocess the video signal in accordance with the results of the cadenceanalysis. For example, the results of the cadence analysis (e.g. thedetermined field pairings and/or the determined content type and/orcadence of the content) are provided with the fields of the video signalto the frame processing module 209 which constructs the frames of thevideo signal accordingly. As is shown in FIG. 1a , if the results of thecadence analysis indicate that the video signal 102 includes interlacedcontent then the de-interlacer 210 generates the fields (e.g. A′, B′,C′, etc.) which were not included in the video signal to therebygenerate the full frames of the video content 104. However, as is shownin FIGS. 1b to 1d , if the results of the cadence analysis indicate thatthe video signal (e.g. 106, 110 or 114) includes progressive contentwith a particular cadence (e.g. 22, 32 or 2332) then the de-interlacer210 copies some of the fields in accordance with the fields pairings tothereby generate the full frames of the video content (108, 112 or 116).If the results of the cadence analysis indicate that the video signal102 includes static content then since the content of the fields hasn'tchanged (i.e. the content is static), for the purpose of thisdescription, it doesn't matter whether the content is processed asinterlaced or progressive content, and the de-interlacer 210 can copysome of the fields to generate the fields which were not included in thevideo signal to thereby generate the full frames of the video content.

If the results of the cadence analysis have the low confidence type thenthis may indicate that there are some errors in the motion estimation,and some error correction may be performed on the video signal (thedetails of which are outside the scope of this description). However, itcan be useful to detect the low confidence content in the cadenceanalysis module 204 because this can avoid wasting processing resourcesby attempting to process the content incorrectly before determining thatthere are errors in the content. In particular, by identifying blockswhich have the low confidence type, the detrimental impact these blockscan have on the cadence detection algorithm is limited. Some furtherprocessing of the video signal may be implemented in further processingmodules 212 of the video processing unit 202 before the processed videosignal is output from the video processing unit 202, but the details ofthe further processing that may be applied are beyond the scope of thisdescription.

In the examples described above, a histogram is determined (in stepS314) for cadence signatures of blocks of a field which are assigned tothe progressive content type, in response to determining that the numberof blocks in the field assigned to the progressive content type exceedsa threshold (in step S310). It will be apparent to a person skilled inthe art that the concepts described above involving the use of thehistogram can be applied more generally. In particular, the concept ofidentifying significant cadence signatures from a histogram of cadencesignatures for blocks of a field may be used without necessarilydetermining the content type of blocks as progressive content blocks. Inthese more general examples, cadence signatures may be determined forblocks of a field, e.g. as described above based on the motionindicators.

For example, a cadence signature may be determined for each of theblocks of a field. A histogram can then be created for the determinedcadence signatures of blocks of the field. The histogram may be createdfor cadence signatures of all of the blocks of the field, or for cadencesignatures of a subset of the blocks of the field, e.g. for only theprogressive blocks of the field as in the examples described above, orfor only the blocks within a particular region of the field, or someother subset of the blocks of the field. The position of one or morepeaks in the histogram can be determined to thereby determine one ormore significant cadence signatures in the field. As described above,peaks in the histogram may be considered to indicate significant cadencesignatures if the peaks are above a threshold. Once the significantcadence signatures have been determined, the fields of the video signalcan be processed in accordance with the determined significant cadencesignatures to determine frames of the video signal as described herein.

Often, many of the blocks in a field have the same cadence, so they willhave similar cadence signatures (with some possible variation in thecadence signatures, e.g. due to errors). The use of a histogram allowssignificant (or “prevalent”) cadence signatures to be determined fromthe blocks in the field by identifying which cadence signaturescorrespond to significant peaks in the histogram. This method istherefore robust to errors in the cadence signatures because errors incadence signatures will not tend to lead to significant peaks in thehistogram, such that an erroneous cadence signature will not tend to beidentified as a significant cadence signature when analysing thehistogram. The significant cadence signatures which are identified fromthe histogram may be mapped to known cadence signatures of knowncadences as described above, for example by finding the smallestdistance (e.g. smallest Hamming distance) between the significantcadence signature and a known cadence signature. Alternatively, a set ofknown cadences might not be used, and the significant cadence signaturescan simply be used to identify cadences which would exhibit thosesignificant cadence signatures. This can help to identify unusual orunexpected cadences in the video signal, which might not be included ina set of known cadences. Furthermore, if a set of known cadences is notused, there is no need to decide which cadences should be included inthe set, e.g. by trying to predict which cadences are likely to occur inthe video signal, and there is no need to store the set of knowncadences.

In examples described above, the significant (or “prevalent”) cadencesignatures are determined by analysing a histogram of the cadencesignatures in a field to find peaks which are above a threshold. It willbe apparent to a person skilled in the art that the prevalent cadencesignatures may be determined in other ways. For example, a cadencesignature which occurs frequently within a region of particular interestin the field (e.g. near the centre) may be determined to be a prevalentcadence signature even if it does not occur frequently outside of theregion of particular interest in the field. Irrespective of how theprevalent cadence signatures are identified from the determined cadencesignatures of the blocks of the field, each of the prevalent cadencesignatures can be mapped to a known cadence from a set of known cadenceswhich have known cadence signatures by determining which of the knowncadence signatures of the known cadences has a smallest distance (e.g. asmallest Hamming distance) to the prevalent cadence signature, asdescribed above.

In the examples given above there is a single motion estimator 208 inthe motion estimation module 206. FIG. 7 illustrates some more detailsof the motion estimation module 206. The motion estimation module 206includes the motion estimator 208, a first pixel shifting module 702 ₁and a second pixel shifting module 702 ₂. The pixel shifting module 702₁ is arranged to receive the content of a previous field, and the pixelshifting module 702 ₂ is arranged to receive the content of the currentfield. Outputs of the two pixel shifting modules 702 ₁ and 702 ₂ arecoupled to inputs of the motion estimator 208. The motion estimator 208is a backward motion estimator such that it is configured to compareblocks of the current field with content from a previous (e.g. theimmediately preceding) field from the video signal in order to determinea likely motion vector (referred to as a backward motion vector)describing the motion of the content between the previous field and thecurrent field. However, since consecutive fields of the video signalrelate alternately to odd and even lines of the frames of the videocontent the previous and current fields do not relate to the same linesof content. Therefore, before the motion estimator 208 can compare thecontent of the two fields, the pixel shifting module 702 ₁ shifts thelines of the previous field by a quarter of a pixel of the field (i.e.half a pixel of the full frame), and the pixel shifting module 702 ₂shifts the lines of the current field, in the other direction, by aquarter of a pixel of the field (i.e. half a pixel of the full frame).The shifts may be implemented using interpolation techniques. Theshifted content is then provided to the motion estimator 208. In thisway, the content of the two fields provided to the motion estimator 208are aligned so that the motion estimation can be performed correctly todetermine the backward motion vectors.

However, as demand for improved quality continues and the resourcesavailable increase it is becoming more common to perform multiple motionestimations per block. FIG. 8 shows an example of a motion estimationmodule 800 which comprises: (i) a backward motion estimator 804 similarto the motion estimator 208, which is configured to generate backwardmotion vectors for blocks of the current field by comparing the blocksof the current field with content of the previous field; (ii) abidirectional motion estimator 806 which is configured to generatebidirectional motion vectors for blocks of the current field bycomparing content of the previous field with content of the next field;and (iii) a forward motion estimator 808 configured to generate forwardmotion vectors for blocks of a current field by comparing the blocks ofthe current field with content of the next field. The motion estimationmodule 800 also comprises three pixel shifting modules 802 ₁, 802 ₂ and802 ₃, which are arranged to apply pixel shifts respectively to thecontent of the previous field, the current field and the next field. Asdescribed above, the pixel shifting modules 802 shift the lines of therespective fields (e.g. by interpolation) by a quarter of a pixel of thefield (i.e. half a pixel of the full frame) so that the content comparedby the motion estimators is correctly aligned spatially.

The motion estimation module 800 operates such that the backward motionestimator 804 determines the backward motion vectors by comparing theshifted versions of the previous and current fields output from thepixel shifting modules 802 ₁ and 802 ₂. The forward motion estimator 808determines the forward motion vectors by comparing the shifted versionsof the current and next fields output from the pixel shifting modules802 ₂ and 802 ₃. The bidirectional motion estimator 806 determines thebidirectional motion vectors by comparing the unshifted versions of theprevious and next fields. It is noted that the previous and the nextfields relate to the same lines of the frames (i.e. either odd or evenlines) and as such no pixel shifting is needed to align the contentbefore providing the fields to the bidirectional motion estimator 806.

The motion vectors from each of the motion estimators (804, 806 and 808)may be used to determine the cadence signatures for each of the blocksof the current field. That is, the different types of motion vectors areused to determine respective cadence signatures for the blocks of thecurrent field, so in the case of a three motion estimator configurationeach block in the current field may have three corresponding cadencesignatures determined for it. The additional information from theincreased number of motion estimators may be used to improve therobustness of the cadence analysis performed by the cadence analysismodule 204. If all three motion estimators are correct then the cadencesignature for a block derived from the forward motion vectors, S_(f), isa bit-shifted version of the cadence signature of the block derived fromthe backward motion vectors, S_(b). As an equation this can be writtenas:S _(b)&(2^(N-1)−1)=S _(f)>>1  (1)where “&” represents the AND operator and “>>” represents a bit shift tothe right. Furthermore, for progressive content, there should not bemotion in two consecutive fields of the video signal, and as such thebits of the cadence signature for a block derived from the bidirectionalmotion vectors, S_(d), will be ones unless the previous, current andnext fields are all the same. As an equation this can be written as:S _(d) =S _(f) |S _(b)  (2)where “|” represents the OR operator.

It is described below how the different cadence signatures S_(d), S_(f)and S_(b) may be used to assign blocks to one of the types: static,interlaced, progressive or low confidence. If the number of ones in thebidirectional cadence signature S_(d) is greater than the result ofS_(f)|S_(b) for a block then the block may be assigned to the lowconfidence type because this indicates that there is an error somewhere,e.g. in the motion vectors.

If a block is not assigned to the low confidence type then the block maybe assigned to a content type based on the number of bits of the cadencesignatures which are ones. For example, if the number of ones in theforward or backward cadence signatures (S_(f) or S_(b)) for a block arebelow a lower threshold (where, as an example, the lower threshold maybe 1) then the block may be assigned to the static content type and ifthe number of ones in the forward or backward cadence signatures (S_(f)or S_(b)) for a block are above an upper threshold (where, as anexample, the upper threshold may be 5) then the block may be assigned tothe interlaced content type.

The remaining blocks are likely to be blocks of progressive content butfurther checks may be performed to check that the progressive contenttype should be assigned to the blocks. For example, the number of timesthat consecutive ones are found in the forward cadence signature S_(f)is determined and the number of times that consecutive ones are found inthe backward cadence signature S_(b) is determined. For progressivecontent, consecutive ones should not occur, so if the number ofconsecutive ones in either the forward or backward cadence signatures isgreater than a threshold (e.g. 1) then the block is assigned to the lowconfidence type. Furthermore, a check can be performed to see whetherequation 1 given above holds true, i.e. to check whether shifting thebits of the forward cadence signature to the right by one bit positiondoes give the backward cadence signature. Equation 1 should hold true,so if it does not then the block may be assigned to the low confidencetype. If both of these checks are passed then the block is assigned tothe progressive content type.

It is described above how the multiple motion vectors (backward, forwardand bidirectional) can be used to determine the types of content in thecurrent field in step S308. Furthermore, the forward and backwardcadence signatures (S_(f) and S_(b)) can also be used when creating theprogressive cadence signature histogram in step S314. The backward andforward cadence signatures S_(b) and S_(f) can be combined into a singlehistogram for the progressive blocks. The forward cadence signatures areshifted to the right by one bit such that the most recent bit from theforward cadence signature is discarded. In this way the bits of theshifted forward cadence signature and the corresponding bits of thebackward cadence signature relate to the motion between the same pairsof fields of the video signal. The resulting progressive cadencesignature histogram is used as described above in relation to a singlemotion estimator 208. Similarly, in step S320, each block is assigned toa content type and/or cadence based on both the forward cadencesignature and the backward cadence signature of the block. Using bothforward and backward cadence signatures for these steps (determined fromforward and backward motion estimation respectively) increases therobustness of the cadence analysis to errors because more data isconsidered, thereby reducing the effect of a single error.

Furthermore, in the field pairing step (step S312), the most recent bitof the backward cadence signature can be used to determine if thecurrent field matches the previous field in the video signal and themost recent bit of the forward cadence signature can be used todetermine if the current field matches the next field in the videosignal. It can therefore be appreciated that the use of multiple motionestimators in the motion estimation module 206 can provide for a morerobust and accurate cadence analysis.

In the examples described above, the motion estimation module 206 ispart of the video processing unit 202. FIG. 2 shows the motionestimation module 206 as separate to the cadence analysis module 204.However, in some examples, the motion estimation module 206 may beconsidered to be part of the cadence analysis module 204.

Furthermore, in other examples the motion estimation module could be anindependent component to the video processing unit 202. That is, themotion estimation module may be separate to the video processing unit202 and the motion vectors may be received at the video processing unit202 (e.g. at the cadence analysis module 204) from the separate motionestimation module. For example the cadence signatures may be derived aspart of a video decoder with the motion vectors estimated by anindependent video encoder.

It can therefore be appreciated that there is provided a method ofprocessing a video signal to determine cadence signatures (e.g. for usein cadence detection or the determination of field pairings) based onmotion vectors for the blocks of the fields of the video signal. Thefields of the video signal are to be processed (e.g. in the videoprocessing unit 202) in accordance with the determined cadencesignatures (e.g. in accordance with the detected cadences and/or inaccordance with the determined field pairings) to thereby determineframes of the video signal. This provides for reliable cadence analysiswhich is robust to bad edits in the video signal and to video contentincluding multiple cadences.

FIG. 2 shows the video signal being received by the cadence analysismodule 204. In alternative examples, the video signal may bypass thecadence analysis module 204. This is because the cadence analysis module204 does not actually need the video signal itself in order to determinethe cadence and/or field pairings for the blocks of the video signal. Ascan be appreciated from the description given above, the cadenceanalysis is performed based on the motion vectors determined for theblocks of the fields of the video signal, without using the actual videosignal itself. Therefore, provided that the video signal is provided tothe motion estimation module 206 and the motion estimation module 206provides the motion vectors to the cadence analysis module 204, thecadence analysis module 204 does not actually need to receive the videosignal itself. However, in some examples, it may be simpler if thecadence analysis module 204 does receive the video signal such that itcan provide it with the results of the cadence analysis for furtherprocessing, e.g. by the frame processing module 209. In this way thevideo signal might not be passed directly to the frame processing module209 as shown in FIG. 2 and may instead go via the cadence analysismodule 204.

In the examples described above, motion vectors are obtained (e.g. instep S302) from which the binary flags are determined (e.g. in stepS304) to indicate that a block either has motion or does not have motionassociated with it. In other examples, the full motion vectors might notbe obtained and instead just the binary flags might be obtained sincethis is what the cadence analysis module 204 uses in the methodsdescribed above. That is, the cadence analysis module 204 does not needthe full motion vectors to detect the cadence of the video signal and/orto determine the field pairings for the video signal. In general, thecadence analysis module 204 obtains motion indicators for the blocks ofthe fields, wherein the motion indicators may be motion vectors or maybe binary flags which indicate whether there is substantially somemotion in respective blocks or whether there is substantially no motionin the respective blocks. Furthermore, as described above, the motionestimation module 206 is just one example of a motion analysis modulewhich could be used. In other, more general, examples, a motion analysismodule may analyse motion in the video signal without actuallyestimating the motion (e.g. without actually determining motionvectors).

For example, a motion analysis module may perform a frame differencebased analysis, or an analysis to detect the presence of mouse teeth,and the results of such motion analysis could be used for cadenceanalysis in a similar manner to that in which the results of the motionestimation are used in the examples described in detail herein. Themotion analysis module provides indications of the motion in the fieldsto the cadence analysis module 204, but the precise nature of the motionindicators and the way in which they are estimated can be different indifferent examples. It may be particularly useful to implement themotion indicators as the binary flags (rather than the motion vectors)if the de-interlacer in the video processing unit is a motion adaptivede-interlacer, because motion adaptive de-interlacers do not necessarilyimplement motion estimation, so in this case the video processing unitmight not include a motion estimation module. It is simpler to determinethe binary flags compared to determining the motion vectors, so if themotion vectors are not going to be used in the video processing unit forpurposes other than cadence analysis (e.g. when a motion adaptivede-interlacer is used) then it may be advantageous for the cadenceanalysis module to use the binary flags rather than the full motionvectors.

Generally, any of the functions, methods, techniques or componentsdescribed above can be implemented in modules using software, firmware,hardware (e.g., fixed logic circuitry), or any combination of theseimplementations. The terms “module,” “functionality,” “component”,“unit” and “logic” are used herein to generally represent software,firmware, hardware, or any combination thereof.

In the case of a software implementation, the module, functionality,component, unit or logic represents program code that performs specifiedtasks when executed on a processor (e.g. one or more CPUs). In oneexample, the methods described may be performed by a computer configuredwith software in machine readable form stored on a computer-readablemedium. One such configuration of a computer-readable medium is signalbearing medium and thus is configured to transmit the instructions (e.g.as a carrier wave) to the computing device, such as via a network. Thecomputer-readable medium may also be configured as a non-transitorycomputer-readable storage medium and thus is not a signal bearingmedium. Examples of a computer-readable storage medium include arandom-access memory (RAM), read-only memory (ROM), an optical disc,flash memory, hard disk memory, and other memory devices that may usemagnetic, optical, and other techniques to store instructions or otherdata and that can be accessed by a machine.

The software may be in the form of a computer program comprisingcomputer program code for configuring a computer to perform theconstituent portions of described methods or in the form of a computerprogram comprising computer program code means adapted to perform allthe steps of any of the methods described herein when the program is runon a computer and where the computer program may be embodied on acomputer readable medium. The program code can be stored in one or morecomputer readable media. The features of the techniques described hereinare platform-independent, meaning that the techniques may be implementedon a variety of computing platforms having a variety of processors.

Those skilled in the art will also realize that all, or a portion of thefunctionality, techniques or methods may be carried out by a dedicatedcircuit, an application-specific integrated circuit, a programmablelogic array, a field-programmable gate array, or the like. For example,the module, functionality, component, unit or logic may comprisehardware in the form of circuitry. Such circuitry may includetransistors and/or other hardware elements available in a manufacturingprocess. Such transistors and/or other elements may be used to formcircuitry or structures that implement and/or contain memory, such asregisters, flip flops, or latches, logical operators, such as Booleanoperations, mathematical operators, such as adders, multipliers, orshifters, and interconnects, by way of example. Such elements may beprovided as custom circuits or standard cell libraries, macros, or atother levels of abstraction. Such elements may be interconnected in aspecific arrangement. The module, functionality, component, unit orlogic may include circuitry that is fixed function and circuitry thatcan be programmed to perform a function or functions; such programmingmay be provided from a firmware or software update or control mechanism.In an example, hardware logic has circuitry that implements a fixedfunction operation, state machine or process.

It is also intended to encompass software which “describes” or definesthe configuration of hardware that implements a module, functionality,component, unit or logic described above, such as HDL (hardwaredescription language) software, as is used for designing integratedcircuits, or for configuring programmable chips, to carry out desiredfunctions. That is, there may be provided a computer readable storagemedium having encoded thereon computer readable program code forgenerating a video processing unit configured to perform any of themethods described herein, or for generating a video processing unitcomprising any apparatus described herein. That is, a computer systemmay be configured to generate a representation of a digital circuit fromdefinitions of circuit elements and data defining rules for combiningthose circuit elements, wherein a non-transitory computer readablestorage medium may have stored thereon processor executable instructionsthat when executed at such a computer system, cause the computer systemto generate a video processing unit as described herein.

The term ‘processor’ and ‘computer’ are used herein to refer to anydevice, or portion thereof, with processing capability such that it canexecute instructions, or a dedicated circuit capable of carrying out allor a portion of the functionality or methods, or any combinationthereof.

Although the subject matter has been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the subject matter defined in the appended claims is notnecessarily limited to the specific features or acts described above.Rather, the specific features and acts described above are disclosed asexample forms of implementing the claims. It will be understood that thebenefits and advantages described above may relate to one example or mayrelate to several examples.

Any range or value given herein may be extended or altered withoutlosing the effect sought, as will be apparent to the skilled person. Thesteps of the methods described herein may be carried out in any suitableorder, or simultaneously where appropriate. Aspects of any of theexamples described above may be combined with aspects of any of theother examples described to form further examples without losing theeffect sought.

What is claimed is:
 1. A method of processing a video signal in a videoprocessing unit, the video signal having an interlaced format andcomprising a sequence of fields, the method comprising: determiningcadence signatures for blocks within fields of the video signal, whereinthe cadence signatures are indicative of one or more cadences of thevideo signal, and wherein each cadence signature is determined byprocessing a corresponding block from each of a sequence of consecutivefields of the video signal; creating a histogram of determined cadencesignatures of said blocks for a field; and using the histogram todetermine one or more significant cadence signatures in said field;whereby the fields of the video signal are processed in accordance withone or more determined significant cadence signatures to therebydetermine frames of the video signal.
 2. The method of claim 1, furthercomprising obtaining motion indicators for blocks of a plurality offields of the video signal, wherein the obtained motion indicators areused to determine the cadence signatures of the blocks within fields ofthe video signal.
 3. The method of claim 2, wherein the motionindicators are motion vectors.
 4. The method of claim 2, wherein themotion indicators are binary flags, wherein the binary flag for a blockin a particular field has a first binary value if there is substantiallyno motion in the block of the particular field, and wherein the binaryflag for the block in the particular field has a second binary value ifthere is substantially some motion in the block of the particular field.5. The method of claim 4, further comprising concatenating the binaryflags for the block over the sequence of consecutive fields of the videosignal to thereby determine a cadence signature for the block.
 6. Themethod of claim 3, wherein the obtained motion vectors are used todetermine a cadence signature for each of the blocks within theplurality of fields by assigning a binary flag to the block based on theobtained motion vector for the block.
 7. The method of claim 2, whereinsaid obtaining the motion indicators comprises either: (i) determiningthe motion indicators in a motion analysis module of the videoprocessing unit, or (ii) receiving the motion indicators which have beendetermined by a motion analysis module which is separate to the videoprocessing unit.
 8. The method of claim 1, wherein using the histogramto determine one or more significant cadence signatures in said fieldcomprises determining the position of one or more peaks in the histogramfor said field.
 9. A video processing unit configured to process a videosignal, the video signal having an interlaced format and comprising asequence of fields, the video processing unit comprising: a cadenceanalysis module configured to: determine cadence signatures for blockswithin fields of the video signal, wherein the cadence signatures areindicative of one or more cadences of the video signal, and wherein eachcadence signature is determined by processing a corresponding block fromeach of a sequence of consecutive fields of the video signal; create ahistogram of determined cadence signatures of said blocks for a field;and use the histogram to determine one or more significant cadencesignatures in said field; wherein the video processing unit isconfigured to process the fields of the video signal in accordance withone or more determined significant cadence signatures to therebydetermine frames of the video signal.
 10. The video processing unit ofclaim 9, wherein the video signal includes video content of one or moretype from a set of available video content types which have differentcadences, said set of available video content types including aninterlaced content type and a progressive content type, and wherein thecadence analysis module is further configured to: use the determinedcadence signatures to determine the one or more types of the videocontent in the video signal.
 11. The video processing unit of claim 10,wherein the cadence analysis module is configured to: determine acadence signature for each of the blocks in said field; and assign eachof the blocks in said field to one of the video content types based onthe determined cadence signature for that block.
 12. The videoprocessing unit of claim 11, wherein the cadence analysis module isfurther configured to: determine the number of blocks in said fieldwhich are assigned to the progressive content type; wherein saidhistogram is a histogram of the determined cadence signatures of theblocks in said field which are assigned to the progressive content type,and wherein the cadence analysis module is configured to create saidhistogram responsive to determining that the number of blocks in saidfield assigned to the progressive content type exceeds a threshold. 13.The video processing unit of claim 9, wherein the cadence analysismodule is further configured to map the determined one or moresignificant cadence signatures to one or more cadences of the videosignal.
 14. The video processing unit of claim 13, wherein the one ormore cadences are one or more known cadences from a set of knowncadences which have known cadence signatures, and wherein the cadenceanalysis module is configured to map each of the one or more significantcadence signatures to a known cadence by determining which of the knowncadence signatures of the known cadences has the greatest similaritywith the significant cadence signature.
 15. The video processing unit ofclaim 14, wherein the cadence analysis module is configured to determinewhich of the known cadence signatures of the known cadences has thegreatest similarity with the significant cadence signature bydetermining which of the known cadence signatures of the known cadenceshas a smallest distance to the significant cadence signature.
 16. Thevideo processing unit of claim 15, wherein the smallest distance is asmallest Hamming distance.
 17. The video processing unit of claim 11,wherein the cadence analysis module is further configured to, for eachblock of a particular field: determine a respective measure of thesimilarity between the cadence signature of the block and each of: (i)the known cadence signatures of the known cadences, (ii) a cadencesignature indicative of interlaced content, and (iii) a cadencesignature indicative of static content; and assign, to the block, thecadence or content type which has the greatest similarity according tothe determined measures of similarity.
 18. The video processing unit ofclaim 9, wherein the cadence analysis module is further configured touse the determined cadence signatures to derive field pairings ofconsecutive fields which relate to the same time instance of the videocontent.
 19. The video processing unit of claim 18, wherein the motionindicators are binary flags, wherein the binary flag for a block in aparticular field has a first binary value if there is substantially nomotion in the block of the particular field, and wherein the binary flagfor the block in the particular field has a second binary value if thereis substantially some motion in the block of the particular field, andwherein the cadence analysis module is configured to derive the fieldpairings by finding the first binary value in the most recent elementsof the cadence signatures for the blocks of a field.
 20. Anon-transitory computer readable storage medium having stored thereonprocessor executable instructions that when executed cause at least oneprocessor to process a video signal in a video processing unit, thevideo signal having an interlaced format and comprising a sequence offields, the processing of the video signal comprising: determiningcadence signatures for blocks within fields of the video signal, whereinthe cadence signatures are indicative of one or more cadences of thevideo signal, and wherein each cadence signature is determined byprocessing a corresponding block from each of a sequence of consecutivefields of the video signal; creating a histogram of determined cadencesignatures of said blocks for a field; and using the histogram todetermine one or more significant cadence signatures in said field;wherein the fields of the video signal are to be processed in accordancewith one or more determined significant cadence signatures to therebydetermine frames of the video signal.